Monitoring electrical substation networks

ABSTRACT

Systems and a method for forecasting data at noninstrumented substations from data collected at instrumented substations is provided. An example method includes determining a cluster id for a noninstrumented substation, creating a model from data for instrumented substations having the cluster id, and forecasting the data for the noninstrumented station from the model.

CROSS REFERENCE TO RELATED APPLICATIONS

Pursuant to 35 U.S.C. § 371, this application is the United StatesNational Stage Application of International Patent Application No.PCT/US2016/040722, filed Jul. 1, 2016, the contents of which areincorporated by reference as if set forth in their entirety herein.

TECHNICAL FIELD

The present techniques relate generally to Internet of Things (IoT)devices. More specifically the present techniques relate to devices thatcan monitor electrical substations.

BACKGROUND

It has been estimated that the Internet of Things (IoT) may bringInternet connectivity to 50 billion devices by 2020. For organizations,IoT devices may provide opportunities for monitoring and tracking otherdevices and items, including devices in industrial implementations. Forexample, IoT devices may be used to monitor loads and function inelectrical distribution systems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a map of an electrical substation forecasting deployment bylatitude and longitude.

FIG. 2 is a map of a full deployment as indicated by the filled circles.

FIG. 3 is a map of a sparse sub-optimal deployment.

FIG. 4 is a map of more optimal deployment, showing an even distributionof instrumented substations.

FIG. 5A is a drawing of a computing network including a computing cloud,that may be used to monitor electrical substations.

FIG. 5B is a drawing of a computing network including a computing cloud,in communication with a mesh network of IoT devices, which may be termeda fog 528, operating at the edge of the cloud.

FIG. 6 is a block diagram of a system for prediction of values from, anddeployment of, substation monitors.

FIG. 7 is a block diagram of an example of components that may bepresent in a system for forecasting from data received from sparselyinstrumented.

FIG. 8 is a process flow diagram of a method for forecasting values fora particular substation.

FIG. 9 is a schematic diagram of the unsupervised learning operations.The unsupervised learning operations are executed on the unsupervisedmachine learning module described with respect to FIG. 6.

FIG. 10 is a schematic diagram of the supervised learning operations.The supervised learning operations are executed on the supervisedmachine learning module described with respect to FIG. 6.

FIG. 11 is a process flow diagram of a method for concurrent featureselection.

FIG. 12 is the map of FIG. 1, showing the result obtained forforecasting of a noninstrumented substation.

FIG. 13 is a flow chart of a method for optimizing the deployment ofinstrumentation across substations.

FIG. 14 is a schematic diagram of creating instrumentation deploymentplanning artifacts from the stored performance data.

FIG. 15 is a plot of the deployment cost versus the average forecastaccuracy, showing the relationship between the optimized gridconfiguration and the unoptimized grid configurations at a range of costlevels.

FIG. 16 is a plot that may help a user to decide on a deployment budget.

FIG. 17 is a block diagram of a non-transitory, machine readable mediumincluding instructions, which when executed, direct a processor togenerate forecasts for noninstrumented substations and to predictforecasting accuracy for particular deployments.

The same numbers are used throughout the disclosure and the figures toreference like components and features. Numbers in the 100 series referto features originally found in FIG. 1; numbers in the 200 series referto features originally found in FIG. 2; and so on.

DESCRIPTION OF THE EMBODIMENTS

The internet of things (IoT) is a concept in which a large number ofcomputing devices are interconnected to each other and to the Internetto provide functionality and data acquisition at very low levels. Forexample, IoT networks may include commercial and home automationdevices, such as water distribution systems, pipeline control systems,plant control systems, light switches, thermostats, locks, cameras,alarms, motion sensors, and the like. These devices, termed IoT devicesherein, may be accessible through remote computers, servers, and othersystems, for example, to control systems or access data. In one example,IoT devices may be used to monitor electric power distribution systemsby instrumenting electrical substations to monitor current, voltage, andother parameters that may be used to track power demand. A substationincludes a grouping of transformers at a terminus of a high voltage lineto step down the high voltage to lower levels for local feeder lines,for example, from 220 kilovolt (kV) to 66 kV, among others. However, asused herein, the term may include any other units in a powerdistribution system, such as smaller transformers feeding housingsubdivisions or city blocks, local transformers, electrical lines, andthe like.

Monitoring the load at electrical distribution substations allows gridstakeholders to better understand and plan the utilization and capacityof the electrical grid infrastructure. The resulting load data alsoallows analytics algorithms to be deployed to forecast the future activeand reactive power demand on the substations. However, instrumentationmay often be placed at all substations for which forecasts are required,increasing costs. The techniques discussed herein allow a sparsedistribution of instrumentation across substations to be used to provideload forecasts across instrumented and noninstrumented substations.

Unsupervised machine learning techniques are used to determine whichinstrumented substations are the most representative of the behavior ofa noninstrumented substation. Then data from the relevant instrumentedsubstations may then be combined with external data sources, such asdata from temperature sensors, humidity sensors, weather predictions,and the like. The combined data may then be used in models, for example,generated by supervised machine learning techniques, to provideforecasts of the power consumption of substations which do not haveinstrumentation. The benefit to the information consumer is that fullsmart grid analytics may be enabled with reduced deployments costs,since only a subset of substations need to be instrumented.

In addition to predicting values for noninstrumented substations, thetechniques provide methods for planning for the deployment of thesystem. The optimal deployment strategy may maximize forecastingperformance across the entire sparsely instrumented smart grid, forexample, determining which substations should have instrumentation. Thelevel of forecasting performance expected for a particular investment insmart grid instrumentation may be estimated, for example, to decreasethe likelihood of overspending on the instrumentation.

This deployment prediction includes a smart grid deployment forecastingoptimization system and associated calculation techniques. These systemsand techniques allow a stakeholder to generate optimal sparseinstrumentation deployment strategies and estimate in advance how muchinvestment is needed to achieve a prescribed level of forecastingperformance, based on statistical models from previous deployment data.Thus, full smart grid analytics may be enabled with reduced deploymentscosts, since only a subset of substations may be instrumented.

The smart grid deployment forecasting method includes a combination of asystem and algorithms to generate deployment planning tools, such asgraphs, lookup tables, spreadsheets, and the like, for utilitystakeholders. Generally, the systems use historical deployment data fromprevious deployments to generate forecast performance data forsub-optimal and optimal subsets sets of instrumented substationsthroughout the grid for various levels of deployment budget. Models arebuilt on the resulting performance-budget data to allow smart gridstakeholders to determine a predicted forecast accuracy performance as afunction of an available instrumentation budget.

FIG. 1 is a map 100 of an electrical substation forecasting deploymentby latitude 102 and longitude 104. In this example, a number ofsubstations 106-112 are instrumented with power sensing equipment, suchas sensors to determine active power, reactive power, voltage, current,harmonic energies, and the like. However, substation 114 is notinstrumented. The power sensing equipment allows a determination ofpower profiles 116, which are plots of the power demand 118 against time120. The techniques described allows the power profile 116 at substation114 to be forecasted, in addition to forecasting the other data typeswhich exist for the instrumented substations 106-112.

All substations 106-114 have metadata which represents thecharacteristics of the substation. The data may be available from thedatabases owned by the utility and other sources, and may includesubstation locations, grid topology data, official utility regionclassifications, substation sizing planning data, census data, and thelike. Since all substations, both instrumented and noninstrumented, havemetadata available, unsupervised machine learning techniques may beapplied to the metadata to determine which substations are most similarto each other and group all substations by similarity.

In the example in FIG. 1, substations 106 and 110 have similar shapepower profiles and substations 108 and 112 have similar shape powerprofiles. For example, substations 106 and 110 may be located inprimarily residential areas and substations 108 and 112 may be locatedin primarily industrial areas. The metadata for both instrumentedsubstations 106-112 and noninstrumented substations 114, may include theproportion of industrial and residential loads each substation 106-114services. If the substations 106-114 are grouped by the availablemetadata features, substation 114 would be assigned to one of the othergroups according to which substations are most similar across allfeatures, as discussed with respect to FIG. 12. Once substation 114 hasbeen grouped with the appropriate instrumented substations, supervisedmachine learning may be used to build forecasting models for substation114 using the data from the instrumented substations with which it wasgrouped. The resulting ‘virtual’ model generated for substation 116 haspredictive performance that may be as useful as if substation 114 wasfully instrumented, with no instrumentation cost.

FIG. 2 is a map of a full deployment 200 as indicated by the filledcircles. Like numbered items are as described with respect to FIG. 1.The state of the art in electrical substation forecasting is toinstrument all substations of interest. If the electrical gridstakeholders wish to forecast the load at a new substation, they need toinvest in instrumenting the substation and tolerate initial periods ofinaccurate forecasts due to insufficient historical data available forthat substation.

Since full deployment and sparse deployment both provide forecasts forall substations, the average substation performance is defined to be theaverage forecasting performance across all substations, regardless ofwhether they are instrumented or noninstrumented. Similarly, the averagecost is the average cost to deploy the load forecasting algorithms toall substations, including both instrumented and noninstrumentedsubstations. For example, if twice as many substations are instrumented,the average cost per forecastable substation doubles.

In the full deployment example, the average substation performance maybe at about 5%, which is mainly from the error of the instrumentsthemselves. To provide a comparison point, the average cost in thisexample may be about $1000.

FIG. 3 is a map of a sparse sub-optimal deployment 300. Like numbereditems are as described with respect to FIG. 1. In the sub-optimaldeployment 300 the instrumentation is deployed in a tight cluster 302,providing a poor representation of the entire grid's behavior. Theaverage cost may be lower, for example, at about $250 per substation,but the average error is substantially higher, for example, at about30%.

By running simulations and optimizations on data from various previousdeployments, the techniques described herein allow a grid stakeholder toassess the levels of performance that may be achieved for a given levelof investment. Further, the techniques allow a stakeholder to comparedeployment strategies that improved forecasting performance in previousdeployments. This information may be used to plan sparse infrastructuredeployment and expected costs to obtain acceptable error levels for agiven cost.

FIG. 4 is a map of more optimal deployment 400, showing an evendistribution of instrumented substations. Like numbered items are asdescribed with respect to FIG. 1. In this deployment, the average costis also around $250 per substation, but the average error, at about 10%,is substantially lower. Thus, the grid stakeholders do not need toinstrument all of the substations to get load forecasts for allsubstations.

As described with respect to FIGS. 2-4, sparse deployments lead to lowerperformance, but allow deployments at reduced costs. In the more optimaldeployment 400 of FIG. 4, the performance is reduced by a factor of two,but the total cost of deployment is reduced by a factor of four. Thus,smart grid stakeholders may significantly reduce their total cost ofdeployment by accepting lower forecasting accuracy. As no forecasts canbe 100% accurate, due to instrument error and other factors, a smartgrid stakeholder may be able to choose what level of accuracy isacceptable for a given price point. Techniques herein describe a systemto enable a grid stakeholder to intuitively assess thiscost-vs.-accuracy tradeoff and choose their necessary level ofinstrumentation. But, core to that innovation is the methods and systemin this invention to enable forecasting at noninstrumented substations.

To enable electrical substation forecasting in the presence of sparseinstrumentation, the techniques described specify a system architectureand three combined algorithms to forecast future load over time forsubstations which are not instrumented. The techniques couple a dataingestion and machine-learning system with a unique combination ofmachine learning algorithms that are tailored for the use-case. The nextsections will describe each of these components in turn.

Further, as described herein, the techniques enable a determination ofwhich substations should be instrumented to achieve the desired levelsof predictive performance. To achieve this, the techniques are extendedby a subsystem that can perform simulations of the achievableperformance when different substations are instrumented. The simulationsmay be used to determine the optimal selection of instrumentedsubstations. The extended system is coupled with algorithms to model andcommunicate the predictive performance achievable for a grid deployment.

FIG. 5A is a drawing of a computing network 500 including a computingcloud 502, that may be used to monitor electrical substations. The cloud502 may include a local area network (LAN), a wide area network (WAN),or the Internet.

The electrical substations may be monitored by IoT devices, e.g.,substation monitors (SSMs) 504, that are clustered into a group 506, forexample, by distribution system or region, among others. The SSMs 504may communicate with a gateway 508 over a network 510, which may beprovided by radio communications, a wired network, or any combinations.For example, a network 510 may be used to communicate with SSMs 504 in aparticular substation or area, while a radio network, such as asatellite uplink, a low power wide area network (LPWAN), an LTE network,and the like, may be used for communications between the gateway 508 andthe cloud 502.

As noted, the forecasting techniques may use any number of other datasources. These may include, for example, a metadata database 512, aweather database 514, and a scheduling database 516, among others. Themetadata database 512 may provide the metadata about particularsubstations, as described herein. The weather database 514 may providecurrent weather information across a region, as well as forecasts. Thescheduling database 516 may provide work and holiday schedules forindustries and regions. Any of these databases 512-116 may be associatedwith private or governmental organizations.

The computing network 500 may also include any number of different typesof IoT devices for providing other types of data to the forecastingsystem. The IoT devices may include remote weather substations 518,temperature sensors 520, traffic flow monitors 522, and any number ofother devices, such as home thermostats. The IoT devices may becommunicating through the cloud 502 with a server 524, for example, topredict substation performance for a noninstrumented substation or topredict the best locations for instrumenting substations.

The computing network 500 does not have to be a linear communicationsnetwork, but may include a mesh network or fog of devices. This isdescribed further with respect to FIG. 5B.

FIG. 5B is a drawing of a computing network 526 including a computingcloud 502, in communication with a mesh network of IoT devices, whichmay be termed a fog 528, operating at the edge of the cloud 502. The fog528 may be considered to be a massively interconnected network wherein anumber of IoT devices are in communications with each other and with thegateways 508, for example, by radio links (shown as dashed lines). Theradio communications may be implemented by radios compliant with theIEEE 802.22 standard for wireless regional networks, compliant with theIEEE 802.15.4 standard for low power wide area networks, and the like.

The communications may be performed using the open interconnectconsortium (OIC) standard specification 1.0 released by the OpenConnectivity Foundation™ (OCF) on Dec. 23, 2015. This standard allowsdevices to discover each other and establish communications forinterconnects. Other interconnection protocols may also be used,including, for example, MQTT, CoAP, and the like.

The fog 528 may include any number of different IoT devices. Forexample, three types of IoT devices are shown in the fog 528 of FIG. 5B,gateways 508, SSMs 504, and data aggregators 530. However, anycombinations of IoT devices and functionality may be used. The dataaggregators 530 may be included to collect and process data from theSSMs 504, providing local computing support and data storage in the fog528. This may be useful for locating the prediction service, forexample, as described with respect to FIG. 6, within the fog 528. Insome examples, the data aggregators 530 may be omitted, and the SSMs 504handle all of the functions in the fog 528. In this example, theprediction service may be located in the server 524, or in other systemsin the cloud 502.

The gateways 508 are the edge devices that provide communicationsbetween the cloud 502 and the fog 528. The fog 528 of the IoT devicesmay be presented to devices in the cloud 502, such as a server 524, as asingle device located at the edge of the cloud 502, for example, as afog 528 device. In this example, the alerts coming from the fog 528device may be sent without being identified as coming from a specificIoT device within the fog 528. For example, a prediction may indicate apower demand for a substation, but may not necessarily identify whetherthe prediction is based on an instrumented substation, a noninstrumentedsubstation, or a substation whose instrumentation has failed. Thisinformation may be presented as lower level or “drill down” informationin a user interface.

In some examples, the IoT devices may be configured using an imperativeprogramming style, for example, with each IoT device having a specificfunction. However, the IoT devices forming the fog 528 device may beconfigured in a declarative programming style, allowing the IoT devicesto reconfigure their operations and communications to determine theresources needed to respond to conditions, queries, and device failures.For example, a query from a user located at a server 524 about the powerdemand at a substation may result in the fog 528 device selecting theIoT devices, such as the gateways 508, data aggregators 530, or SSMs504, needed to answer the query.

FIG. 6 is a block diagram of a system 600 for prediction of values from,and deployment of, substation monitors. As described with respect toFIG. 5A, the metadata for the noninstrumented substations (shown as opencircles) may not actually come from the substation itself, but fromutility systems or auxiliary databases. Metadata for instrumentedsubstations (shown as filled circles) may be provided by theinstrumentation, or sourced from the database. However, it isrepresented as coming from the device here to clarify which device thedata refers to. The system 600 may be located in a server 524 in a cloud502, as described in FIG. 5A, or in a data aggregator 530 or otherdevice, located in a fog 528 device, as described with respect to FIG.5B.

The system 600 can perform the methods described with respect to FIGS.8-11 and 13-16. The methods may be performed upon reception of new, orsignificantly changed, data. Further, the methods may be repeated on aperiodic basis, or triggered by an external user. The reception of newdata can occur in a variety of ways. For example, data may be polled bythe system 600 from substations 602, or external data sources 604, suchas utility databases 606, third-party data sources 608, or IoT sensors610. Further, data may be pushed into the system by external datasources 604 or directly by a substation 602. Data may also be manuallyuploaded 612 to the system 600, for example, by a grid stakeholder. Thedata may be saved in local stores within the system 600, for example, ina metadata database 614 and a historical database 616. An auxiliary datasource manager 618 may control the intake of data from the external datasources 604, among other functions.

The components of this system 600 interact with each other to produceforecasts 620 for both instrumented and noninstrumented substationsalike. Forecasts 620 for noninstrumented substations use an unsupervisedmachine learning module (UMLM) 622 to produce substation groupings 624to deduce which instrumented substations to use on aggregate to generatemodels. In contrast, forecast 620 for instrumented substations generallydo not need the UMLM 622, because a model can be generated directly fromthe data available for that substation.

The main components of the system 600 include a data aggregator 530.This component may or may not exist in a particular deployment. The dataaggregator 530 may retrieve and aggregate data from several substations602, and then provide the relevant data to the system 600. The dataaggregator 530 may also provide metadata for several noninstrumentedsubstations to the system 600. Further, in some examples, as describedwith respect to FIG. 5B, the data aggregator 530 may also include theentire system 600. If the data aggregator 530 is not present in a givendeployment, instrumented substation data may arrive directly from theinstrumented substations.

A data parsing and routing component (DPR) 532 receives data fromexternal systems, such as instrumented substations and the dataaggregator 530. The DPR 532 may be programmed to parse the data andinsert the relevant metadata into the metadata database 614 andhistorical data into the historical database 616.

The metadata database 614 stores the substation metadata. As describedherein, the metadata changes infrequently or never, such as substationratings, instrumentation characteristics, substation geographicalcoordinates, and the like. The metadata is available for all substationswhich need to be forecasted, both instrumented and noninstrumented.

The historic substation data is stored in the historical database 616.In contrast to the metadata, the historical data changes as a functionof time. The historic substation data stored in the historical databasemay include active power, reactive power, voltage, and the like forforecasting performance of a substation. The historic data is generallyonly available in sufficient quantities for instrumented substations.Further, the historic data may include data used to monitor gridperformance and substation dynamics. For example, these may include trueor active power (P), apparent power (S), reactive power (Q),voltage-ampere reactive (VAR), and the like.

As used herein, active power (P) is measured in units of watts (W), andis calculated as I²R or IE, where I is amperage (amps), R is resistance(ohms), and E is voltage (volts). Reactive power (Q) is a measure of thepower draw from the reactance of the inductive and capacitive loads, andis calculated as I²X or E²/X, where X is a function of the circuit'sreactance. Apparent power (S) is a measure of the power draw from theimpedance of the circuits, and is calculated as I²Z or E²/Z, where Z isa function of the circuit's impedance. VAR is an abbreviation of, and ismeasures the temporary draws from inductive loads.

Some of these measurements have acute consequences and some more chronicconsequences. For example, when these operational parameters areexceeded, equipment may fail and substations may go off line.Accordingly, the forecasts for the noninstrumented stations may use thisinformation to forecast potential equipment failures in thenoninstrumented stations.

The UMLM 622 may retrieve metadata from the metadata database 614 andexecute an unsupervised learning (clustering) algorithm, as describedherein. It then passes relevant results, such the substation groupings624, to the supervised machine learning module (SMLM) 626. The UMLM 622may also store the result and other relevant metadata back into themetadata database 614.

The SMLM 626 receives data from the UMLM 622, the metadata database 614,the historical database 616, and the auxiliary data source manager 618and executes the supervised learning (forecasting) algorithms describedbelow. It then stores the forecasts and the relevant data in themetadata database 614 and the historical database 616 and makes theforecasts 620 available to external agents, for example, forpresentation on a display 630 at an operator workstation.

As described herein, the auxiliary data source manager 618 retrievesauxiliary data 628 from external data sources 604, either down-samplingor interpolating the auxiliary data 628, storing the auxiliary data 628and presenting the auxiliary data 628 to the SMLM 626. As described,external data sources can include, but are not limited to, weather dataacross diverse geographic regions, utilities' operations databases,utilities' predictive maintenance databases, utilities' geographicalinformation services (GIS) database, social media websites, trafficforecasting services, and the like. The auxiliary data source manager618 may also provide data source discovery, quality assessment andcaching.

The system 600 may also include a performance simulation andoptimization subsystem 632 to run simulations using the existingdatabases and the available supervised and unsupervised learningmodules. Although the system 600 is shown in relation to the previoussparse instrumentation forecasting invention, the performance simulationand optimization subsystem 632 can be deployed on any smart grid or IoTforecasting system that can forecast in the presence of sparseinstrumentation.

The performance simulation and optimization subsystem 632 mayiteratively run simulations of the forecasting performance for a givengrid configuration, including different combinations of instrumented andnoninstrumented substations. This may be performed by triggering theUMLM 622 and SMLM 626 with simulated data, such as modified instances ofdata from the existing databases, and capturing the simulated forecastsbefore they are emitted to external systems. The simulated forecasts arethen fed back into the performance simulation and optimization subsystem632 to allow a comparison with the available historical data to evaluatethe forecasting performance for a current simulated configuration of thegrid.

Generally, the performance simulation and optimization subsystem 632accesses the metadata database 614 and the historical database 616. Foreach budget level, a binary optimization feature vector may be created,in which each element represents a substation and the value of thevector describes whether or not the corresponding substation isinstrumented in the simulation. Unsupervised optimization may be run,which iteratively changes which elements of the feature vector are high,which, in turn, changes which substations are instrumented in thesimulation. At each iteration, a simulated forecast is generated for thesparsely instrumented substations. For each substation the simulatedforecasts are compared with the available historical data to evaluatethe average forecast error across all substations, both instrumented andsimulated noninstrumented. The binary feature vector is iteratedaccording to the chosen optimization algorithm's process. At everyiteration of the optimization algorithm the current un-optimizedperformance-cost pair is stored. At every optimization termination theoptimized performance-cost pair is stored. When the performancesimulation finishes, models and graphs are constructed to enableplanning of deployment costs and optimal deployment strategies.

FIG. 7 is a block diagram of an example of components that may bepresent in a system 700 for forecasting from data received from sparselyinstrumented substations. The system may include any combinations of thecomponents shown in the example, and may be a separate server in a cloudor may be part of a fog device. The components may be implemented asICs, portions thereof, discrete electronic devices, or other modules,logic, hardware, software, firmware, or a combination thereof adapted inthe system, or as components otherwise incorporated within a chassis ofa larger system. The block diagram of FIG. 7 is intended to show a highlevel view of components of the system 700. However, some of thecomponents shown may be omitted, additional components may be present,and different arrangement of the components shown may occur in otherimplementations.

The system 700 may include a processor 702, which may be amicroprocessor, a multi-core processor, a multithreaded processor, anultra-low voltage processor, an embedded processor, or other knownprocessing element. The processor 702 may be a part of a system on achip (SoC) in which the processor 702 and other components are formedinto a single integrated circuit, or a single package, such as theEdison™ or Galileo™ SoC boards from Intel. As an example, the processor702 may include an Intel® Architecture Core™ based processor, such as aQuark™, an Atom™, an i3, an i5, an i7, or an MCU-class processor, oranother such processor available from Intel® Corporation, Santa Clara,Calif. However, other processors may be used, such as available fromAdvanced Micro Devices, Inc. (AMD) of Sunnyvale, Calif., a MIPS-baseddesign from MIPS Technologies, Inc. of Sunnyvale, Calif., an ARM-baseddesign licensed from ARM Holdings, Ltd. or customer thereof, or theirlicensees or adopters. The processors may include units such as an A5,A9, or similar, processor from Apple® Inc., a Snapdragon™ processor fromQualcomm® Technologies, Inc., or an OMAP™ processor from TexasInstruments, Inc.

The processor 702 may communicate with a system memory 704 over a bus706. Any number of memory devices may be used to provide for a givenamount of system memory. As examples, the memory can be random accessmemory (RAM) in accordance with a Joint Electron Devices EngineeringCouncil (JEDEC) low power double data rate (LPDDR)-based design such asthe current LPDDR2 standard according to JEDEC JESD 209-2E (publishedApril 2009), or a next generation LPDDR standard to be referred to asLPDDR3 or LPDDR4 that will offer extensions to LPDDR2 to increasebandwidth. In various implementations the individual memory devices maybe of any number of different package types such as single die package(SDP), dual die package (DDP) or quad die package (Q17P). These devices,in some embodiments, may be directly soldered onto a motherboard toprovide a lower profile solution, while in other embodiments the devicesare configured as one or more memory modules that in turn couple to themotherboard by a given connector. Any number of other memoryimplementations may be used, such as other types of memory modules,e.g., dual inline memory modules (DIMMs) of different varietiesincluding but not limited to microDIMMs or MiniDIMMs. For example, amemory may be sized between 2 GB and 16 GB, and may be configured as aDDR3LM package or an LPDDR2 or LPDDR3 memory, which is soldered onto amotherboard via a ball grid array (BGA).

To provide for persistent storage of information such as data,applications, operating systems and so forth, a mass storage 708 mayalso couple to the processor 702 via the bus 706. The mass storage 708may be implemented via a solid state disk drive (SSDD), a hard drive, anarray of hard drives, and the like. However, in some examples, the massstorage 708 may be implemented using a micro hard disk drive (HDD), suchas in IoT devices. Further, any number of new technologies may be usedfor the mass storage 708 in addition to, or instead of, the technologiesdescribed, such resistance change memories, phase change memories,holographic memories, or chemical memories, among others. For example,the system 700 may incorporate the 3D XPOINT memories from Intel® andMicron®.

The components may communicate over the bus 706. The bus 706 may includeany number of technologies, including industry standard architecture(ISA), extended ISA (EISA), peripheral component interconnect (PCI),peripheral component interconnect extended (PCIx), PCI express (PCIe),or any number of other technologies. The bus 706 may be a proprietarybus, for example, used in a SoC based system. Other bus systems may beincluded, such as an I²C interface, an SPI interface, and point to pointinterfaces, among others.

The bus 706 may couple the processor 702 to a radio transceiver 710, forcommunications with substations 712. The radio transceiver 710 mayinclude any number of frequencies and protocols, such as a WLAN unitused to implement Wi-Fi™ communications in accordance with the Instituteof Electrical and Electronics Engineers (IEEE) 802.11 standard. Inaddition, wireless wide area communications, e.g., according to acellular or other wireless wide area protocol, may be implemented via aWWAN unit. For examples in which the system 700 is an IoT device, theradio transceiver 710 may include a radio for communications at about2.4 gigahertz (Ghz) under the IEEE 802.15.4 standard, for example, usingthe Bluetooth® low energy (BTLE) standard, as defined by the Bluetooth®Special Interest Group, or the ZigBee® standard, among others. Anynumber of other radios, configured for a particular wirelesscommunication protocol, may be included in the radio transceiver 710.

The radio transceiver 710 may include a low power wide area (LPWA)transceiver 713 to communicate with substations 712 over an LPWA link.In this example, the communications may follow the IEEE 802.15.4 andIEEE 802.15.4g standards, among others. For example, the system 700 maycommunicate over a wide area using LoRaWAN™ (Long Range Wide AreaNetwork) developed by Semtech and the LoRa Alliance. The techniquesdescribed herein are not limited to these technologies, but may be usedwith any number of other cloud transceivers that implement long range,low bandwidth communications, such as Sigfox, and other technologies.

The bus 706 may couple the processor 702 to a network interfacecontroller (NIC) 714 that may couple to a cloud 716 over a wiredconnection. The cloud 716 may also connect to external data sources 604,such as databases, external IoT devices, and the like, as described withrespect to FIG. 6. The bus 706 may couple the processor 702 to a sensorinterface 718. The sensor interface 718 may be used to obtain sensorreadings from the external data source 604.

A human-machine interface (HMI) 720 may be included to couple the system700 to various input/output (I/O). For example, a display 722 may beincluded to show information, such as forecasts or deployment plans. Aninput device 724, such as a keyboard or touch screen or keypad may beincluded to accept input for directing the operations.

The mass storage 708 may include a number of modules to implement theforecasting and deployment prediction functions described herein. Thesemodules are as described with respect to FIG. 6.

In examples in which the system 700 is an IoT device, for example, in afog device, a number of other units may be included. For example, abattery may power the system 700. The battery may be a lithium ionbattery, a metal-air battery, such as a zinc-air battery, analuminum-air battery, a lithium-air battery, and the like. A batterymonitor/charger may be included in the system 700 to track the state ofcharge (SoCh) of the battery. The battery monitor/charger may be used tomonitor other parameters of the battery to provide failure predictions,such as the state of health (SoH) and the state of function (SoF) of thebattery. The battery monitor/charger may include a battery monitoringintegrated circuit, such as an LTC4020 or an LTC2990 from LinearTechnologies, an ADT7488A from ON Semiconductor of Phoenix Ariz., or anIC from the UCD90xxx family from Texas Instruments of Dallas, Tex. Thebattery monitor/charger may communicate the information on the batteryto the processor 702 over the bus 706. The battery monitor/charger mayalso include an analog-to-digital (ADC) convertor that allows theprocessor 702 to directly monitor the voltage of the battery, or, withappropriate circuitry, the current flow from the battery.

A power block, or other power supply coupled to a grid, may be coupledwith the battery monitor/charger to charge the battery. In someexamples, the power block may be replaced with a wireless power receiverto obtain the power wirelessly, for example, through a loop antenna inthe system 700. A battery charging circuit, such as an LTC4020 chip fromLinear Technologies of Milpitas, Calif., among others, may be includedin the battery monitor/charger. The specific charging circuits chosendepend on the size of the battery, and thus, the current required. Thecharging may be performed using the Airfuel standard promulgated by theAirfuel Alliance, the Qi wireless charging standard promulgated by theWireless Power Consortium, the Rezence charging standard, promulgated bythe Alliance for Wireless Power, among others.

FIG. 8 is a process flow diagram of a method 800 for forecasting valuesfor a particular substation. The method 800 may be implemented by thesystem described with respect to FIGS. 6 and 7. The method 800 starts atblock 802 when a forecast for a given substation is requested.

At block 804, the quantity and quality of a substation's historical datais requested. This determines whether forecasts can be provided foreither an instrumented or a noninstrumented substation. The data isevaluated at block 806 to determine if the data quality or quantity issufficiently high, for example above an empirically defined threshold.If so, the substation is deemed to be an instrumented substation andprocess flow proceeds to block 808. At block 808, a model generated fromthe substation's available data is accessed or created. At block 810,the forecast for the substation is generated. At block 812, the forecastfor the substation is returned.

If data of insufficient quality or quantity to generate a forecast,process flow proceeds to block 814. At block 814, a determination ismade as to whether substation clusters are up-to-date, either from theage of the previous cluster operation or if the metadata used for thepreviously executed operation has significantly changed since the pastclustering operation. If not, at block 816 the substation clusters areregenerated, for example, using the unsupervised machine learning moduledescribed with respect to FIG. 6. At block 818, the clusteridentification for the substation to be forecast is determined. Thisidentifies the substations that have similar metadata, and are likely tohave similar parameters.

At block 820, a determination is made as to whether the forecastingshould use data aggregation or model aggregation. The determination ismade by evaluating against a flag set by the system user. If by dataaggregation, process flow proceeds to block 822.

At block 822 the data for all substations with the same cluster id asthe requested substation are fetched, and the data is aggregated, forexample, by the weighted or unweighted averaging of the data acrosssamples with the same or similar timestamps. At block 824, a model maybe produced from the aggregated cluster data. At block 826, a forecastis produced from the model. process flow then proceeds to block 812 toreturn the forecast.

If, at block 820, it is determined that the user has selected a modelaggregation, process flow proceeds to block 828. At block 828, modelsare fetched or created for all substations with the same cluster ID asthe requested substation. At block 830, forecasts are generated fromeach of the models for the requested substation. At block 832, all ofthe forecasts are aggregated to generate a single forecast, for example,by averaging together the forecasts over time or by using moresophisticated machine learning ensemble technique. Process flow thenreturns to block 812 to present the forecast for the requestedsubstation.

The method 800 uses a number of algorithms to generate the forecasts forthe requested substation. Two machine learning techniques, unsupervisedlearning and supervised learning, are used to produce online forecastsof noninstrumented substations. A third technique, concurrent featureselection, is used to dynamically update the optimal set of data sourcesemployed for both the unsupervised and supervised learning phase tomaximize predictive performance.

FIG. 9 is a schematic diagram of the unsupervised learning operations.The unsupervised learning operations are executed on the unsupervisedmachine learning module 622 described with respect to FIG. 6. Theunsupervised learning technique that may be utilized is clustering. Anyclustering technique can be employed, for example, hierarchicalclustering, Expectation-Maximization (EM), k-means, density-basedspatial clustering of applications with noise (DBSCAN).

The clustering feature matrix 902 contains all features deemed relevantto this particular clustering operation. The binary feature selectionvector 904, [f1, f2, . . . , fN], may be calculated by a featureselection algorithm. This dictates whether a particular feature vectorwill be included in a particular execution of the clustering algorithm.For example, the substation's name would be a bad feature to include asit has little correlation with the substation's power profile over time,but the substation's average expected power consumption from utilityplanning data would be a good feature for grouping substations. Otherpossible features for grouping include average power, repetitiveness(e.g., average intra-day correlation), the relation of the substation toall others in hierarchy, area load classifications (e.g., 20%residential, 50% industrial, 30% commercial), regional energy tariffs ofcustomers, and the like.

The clustering feature matrix 902 is provided to the UMLM forclustering. In an example, this was performed by a k-means clusteringalgorithm. By applying the clustering technique to the set 904 of datafeatures 906 derived from the substation's metadata, a list of clusterlabels 908 may be generated for each substation. The list of clusterlabels 908 may then be used to generate substation groupings 910 thatinclude all of the similar substations, each with a unique cluster id.These groupings state which substations should be grouped by similarityfor the supervised learning phase. These groupings are employed when aforecast for a noninstrumented substation is requested.

To enable forecast model generation, it is necessary to have at leastone instrumented substation in each cluster. Clustering techniques donot natively include mechanisms to ensure this, hence, this can beachieved with various additional algorithmic steps. For example, acluster without an instrumented station may be merged into the closestcluster which does contain an instrumented station. Further, aninstrumented station closest to the cluster which does not haveinstrumented stations may be merged into that cluster, or the like.

FIG. 10 is a schematic diagram of the supervised learning operations.The supervised learning operations are executed on the supervisedmachine learning module 626 described with respect to FIG. 6. In oneexample, the particular supervised machine learning techniques employedwas regression. The regression techniques may be used to build models oncontinuous-value labelled data to infer labels for future unlabeleddata. Any number of regression techniques may be used to infer labels inthis invention, including linear regression, polynomial regression,artificial neural networks, support vector machines, Gaussian processes,random forests, decision trees, and the like.

The supervised learning operations assume that a set of substationgroupings have already been determined by the unsupervised learningoperations, described with respect to FIG. 9. In this example, aforecast for substation 114 (FIG. 1) has been requested, and it has beenfound that instrumented substations 108 and 112 are most similar to thenoninstrumented substation 114. In this context we will call thesubstations on which the model is based on, substations 108 and 112 thesource substations. For each source substation, there is a featurematrix containing the input or independent features of the model, termedhistoric substation features 1002, and an output or dependent featurevector, termed the historic power features 1004. The generatedregression model maps the translation between the historic substationfeatures 1002 and the historic power features 1004.

The historic substation features 1002 initially contains all availablehistoric data sources. However not all sources are beneficial to thepredictive performance of the model. As described herein, the binaryfeature selection vector [g1, g2, . . . , gN] may be calculated by thetechniques feature selection algorithm, which dictates whether aparticular feature vector will be included in a particular execution ofthe regression algorithm.

One factor in the generation of a forecast for one substation from datafrom a number of substations is the chosen aggregation strategy 1006. Togenerate a single substation's forecast, either the data from allsubstations must be aggregated together into a single dataset beforemodel generation, or multiple forecasts, one per substation, must beaggregated after forecasting from the models occurs. Aggregation isperformed by either data aggregation or model aggregation based on aflag set beforehand as described with respect to FIG. 8. The twotechniques are equally valid and which one to use is dependent on theuse-case and the user's secondary requirements. For example, using thedata aggregation technique may also produce prediction intervals, whichis an estimate of the confidence bounds of the forecasted load overtime, which is of interest to many grid planners.

Model aggregation, on the other hand, does not readily produce forecastprediction intervals because it aggregates the forecasted values, andprediction intervals should not be aggregated across model outputs.However, model aggregation is more efficient as it can reuse the modelswhich may already have been generated for individual substations,whereas data aggregation requires the production of a new model for eachcombination of substations to aggregate.

Examples of candidate independent features are shown FIG. 10. However,this list is not complete as any set of time-varying features that maybe derived from the vicinity of the substation can be used. The featuresection algorithm described with respect to FIG. 11, may establish whichfeatures are the most relevant to maximizing forecasting performance.

FIG. 11 is a process flow diagram of a method 1100 for concurrentfeature selection. The method 1100 deduces the optimal combination ofclustering features (F) and regression features (G) from all availablefeatures. As described herein, any number of auxiliary features may beincluded in the clustering, for example, of static features, and theforecasting, for example, of time-varying features. The clusteringalgorithm and the feature selection algorithm will automaticallyestablish which features are relevant to maximize noninstrumentedsubstation forecasting performance. Feature selection is a standardapproach to rigorously deducing which subset from a set of candidatefeatures are most appropriate for a given unsupervised or supervisedlearning model. Feature selection usually occurs for a single model at atime as the features are chosen for the regression model to maximize itsperformance. However, the technique described herein perform featureselection for two cascaded models concurrently.

The method 1100 starts at block 1102. At block 1104, a binary list ofavailable clustering features (F) is compiled. Such clustering featuresmay include the substation's average expected power consumption fromutility planning data, average power, repetitiveness (e.g., averageintra-day correlation), the relation of the substation to all others inhierarchy, area load classifications (e.g., 20% residential, 50%industrial, 30% commercial), regional energy tariffs of customers, andthe like. At block 1106, a binary list of available forecasting features(G) is compiled. Examples of such features are shown in FIG. 10 as thehistoric substation features 1002.

At block 1108, a merged binary vector of the active features (A) iscreated. In the binary vector A, each binary element represents whetherthe corresponding feature is used in the models. On each evaluation theA vector is split into its constituent F and G components, then the Fand G binary vectors are applied to the clustering and regression modelsrespectively and the predictive performance is evaluated.

At block 1110, the values of the active feature vector A are randomlyinitialized. The initialized vector is then set as the current bestvalue, for example, best_A=A. At block 1112, the best average predictiveerror is initialized to be infinity (inf), for example, indeterminatelylarge. At block 1114, the current best feature vector (best_A) is copiedinto the active feature vector (A). At block 1116, an element in A israndomly picked, and the active feature bit for that feature isinverted. This classifies the feature as not being important to theprediction, for example, being eliminated from the calculations.

At block 1118, a determination is made as to whether F and G componentsof A have at least one binary-active element each. This is performed toconfirm that at least one input feature is present for the clustering orregression models to perform the noninstrumented substation forecastingoperation. This may be applied to any machine learning task where anumber of machine learning models are cascaded together to achieve afinal use-case or application with associated performancecharacteristics. If not, process flow returns to block 1114, to invertanother feature bit, activating that feature for the analysis.

At block 1120, the A vector is divided into the F and G vectors for theevaluation. At block 1122, the predictive performance is evaluatedacross all available substation data for the current F set forclustering and the current G set for active forecasting features. Atblock 1124, a determination is made as to whether the current predictiveperformance is better than the previous predictive performance. If so,at block 1126 the current predictive performance, e, and the currentvector, A, are overwritten with the current values for e and A.

At block 1128, a determination is made as to whether the method 1100should be terminated. The criteria that may be used in the evaluationinclude, for example, the number of iterations since a decrease inforecasting error or if the error is below a given threshold. Othercriteria may include whether a previously unexplored feature set isidentified in y iterations. If any or all of these criteria areevaluated to be true, at block 1130, the method 1100 terminates. Thebest_A vector is stored for future execution of the noninstrumentedsubstation forecasting executions.

The method 1100 may be performed in parallel to the main functions ofthe noninstrumented substation forecasting. For example, it may beperiodically executed in batch mode to perform a simulation thatreplicates the behavior of the system 600 described with respect to FIG.6, to determine the optimal set of model features for the system 600.This set of features is then stored and used next time a noninstrumentedsubstation forecast occurs.

FIG. 12 is the map of FIG. 1, showing the result obtained forforecasting of a noninstrumented substation. The techniques allowestimation of a sensor profile 1202, such as active power, reactivepower, voltage, and the like, at a noninstrumented substation, such assubstation 114. Grouping substations by similar metadata, for example,using unsupervised machine learning, substantially improves substationforecasting performance over aggregating data or models from allsubstations. Further, the ability to predict from sparse instrumentationis more economical than instrumenting all substations. This has beenshown lead to substantial deployment cost reduction in the field and canenable other deployment cost reduction services for smart gridstakeholders.

Although the techniques described herein are focused on substationforecasting, they may be generalized to any use case that has IoTdevices producing historical sensor data across a variety of deploymentenvironments where metadata describing the static characteristics of thedeployment environments is available. This allows the production offorecasts for other deployment environments for which we have metadatabut do not have IoT devices deployed.

The examples described herein were tested in a real deployment in whichsubstation data is sent to an analytics service as part of a powerproject. In this test deployment, concurrent feature selection wasperformed manually. Thus, clustering occurs on the metadata as new dataarrives into the platform and models and forecasts are produced fornoninstrumented substations. However, the method 1100 for concurrentclustering may be implemented with the forecasting. The determination ofwhich substations should be instrumented in a sparse deployment affectsboth the total costs and the accuracy of the forecasts.

FIG. 13 is a flow chart of a method 1300 for optimizing the deploymentof instrumentation across substations. The performance simulation andoptimization subsystem 632, described with respect to FIG. 6, mayinclude three components to generate deployment data and models for useby the stakeholder. These components are a performance optimizer, aPareto frontier generator, and a maximum-likelihood cost modelgenerator.

The performance optimizer generates empirical data for forecastingperformance as a function of the subset of substations which areinstrumented. For each chosen cost constraint, for example, deploymentcost, number of instrumented substations, or percentage of substationswhich are instrumented, among others, forecasting performance data isacquired for both the optimized grid configuration and for severalunoptimized grid configurations for the specific cost constraint. Themethod 1300 generates the data using the available historical substationdata and the available unsupervised and supervised machine learningmodules.

The method 1300 begins at block 1302, for example, when a user starts adeployment simulation. At block 1304, the costs constraints areinitialized, for example, to 0% of maximum.

At block 1306, the cost is iterated by a predetermined number ofpercentage points, for example, 1%, 5%, 10%, or more. Block 1306 marksthe start of the outer loop. The task of the outer loop is toiteratively provide increasing values of cost constraints for eachexecution of an inner loop 1307.

At block 1308, a set of initial state features that satisfies the costconstraints is generated. Block 1308 marks the start of the inner loop1307. The inner loop 1307 takes the cost constraints and iteratestowards the highest-performing configuration of the state features thatsatisfy the currently specified cost constraints. The state features arespecified in a binary feature vector in which each element correspondsto a single substation in the grid and the binary value of the elementindicates whether that substation is instrumented for that run. Afeature vector H may be defined as [substation1, substation2,substation3, . . . , substationN], for example, [1, 0, 1, . . . 1].

Higher instrumentation density and the resulting higher deploymentcosts, will generally provide higher overall forecasting performance.Accordingly, the inner loop will naturally tend to set as many Helements high as possible without exceeding the specified costconstraint. Hence, the optimization will automatically push the cost ofdeployment as high as possible without going over the specified costconstraints, so the deployment cost will approach the cost constraintspecified by the outer loop by the end of each inner loop. The number ofinstrumented substations will generally be the same for a specific costconstraint, but the exact subset of substations which are instrumentedwill iteratively change and lead to higher performance.

At block 1310, a determination is made as to whether cost constraintsare satisfied. If not, at block 1312, the state features are iterated,for example, to change the number of instrumentation packages to bedeployed to substations, and the cost constraints are recalculated. Theinput to the evaluation is the feature vector, H, which describes theexample grid configuration to evaluate on this iteration. The strategyfor iterating the state features controls the progress through thepossible configurations of instrumentation in the grid. Any number ofmeta-heuristic optimization techniques that can support binary featuresmay be used, such as genetic algorithms, simulated annealing, particleswarm optimization, hill climbing, tabu search, and the like. If weencounter a state which exceeds the cost constraints, all optimizationtechniques have their own strategies to return to a valid state beforeattempting another iteration. Further, meta-heuristic optimizationtechniques may be used to evaluate multiple hypotheses on eachiteration, allowing high parallelization of this process. For example,particle swarm optimization defines an algorithm in which severalhypotheses, referred to as particles, concurrently exist and the costfor each particle is evaluated at the same time. The lowest costparticle historically encountered for a given particle ID is stored andthe lowest cost particle across all particles is stored at eachiteration. Then the movement of each particle on the next iterationoccurs as a function of the lowest cost example of that particle and thelowest cost example of all particles.

At block 1314, the average substation network performance is evaluated.The performance for one substation may be defined as the multipleday-ahead average forecasting percentage error, for several forecastingdays throughout the period of data availability. Accordingly, theperformance for all substations may be defined as the average of theforecasting performance for each substation, including both instrumentedand noninstrumented substations. When a substation is fully instrumentedand has sufficient historical data, the forecasts occur using modelsbuilt on historical data for that substation. When the substation is notinstrumented, the forecasts occur using the techniques described herein.

At block 1316, a determination is made as to whether the optimizationtermination criteria have been reached. At every iteration an evaluationis made as to whether a sufficient number of iterations have been made,or if the iterations should continue. Each meta-heuristic optimizationalgorithm may have termination criteria that are specific to thealgorithm, but general criteria which can be used are no decrease inforecasting error in a predetermined number of iterations, error ratesbelow a given threshold, or encountering a previously unexplored featureset in y iterations, among others.

At block 1318, the cost and performance data for optimized andun-optimized locations is stored. The current [cost, performance] datapairs are stored to enable the generation of instrumentation deploymentplanning artifacts. This is done for both optimized configurations andfor every un-optimized configuration of the grid. Although intermediatestates evaluated before the optimal has been found during ameta-heuristic optimization process are generally not useful, it hasbeen noted that these values correspond to evaluations of sub-optimalconfigurations of the grid, which is useful for the next modellingsteps. The data is used to build extra models to inform the customerabout the average-case performance they can expect from a deploymentwithout optimizing the deployment strategy. Process flow then returns toblock 1312 to repeat the iteration.

At block 1320, the optimized cost and performance are stored. At block1322, the optimization for a given deployment cost constraint isfinished. At block 1324, a determination is made as to whether the costconstraint has reached 100% of maximum, If not, process flow returns toblock 1306 to continue the outer loop. If so, at block 1326, theinstrumentation deployment planning artifacts on the deployment aregenerated from the stored performance data.

FIG. 14 is a schematic diagram 1400 of creating instrumentationdeployment planning artifacts from the stored performance data. When theperformance simulation and optimization 1402 (method 1300 described withrespect to FIG. 13) has completed, the next step is to generate modelsfor optimal performance and average-case un-optimized performance forchosen cost deployment budgets. If we did not have the optimized pairs,standard algorithms could be used to determine the Pareto frontier fromall the available un-optimized [cost, performance] data 1406. However,the optimized [cost, performance] pairs 1408 were stored for each costconstraint making this unnecessary.

From the un-optimized [cost, performance] data 1406, themaximum-likelihood cost model 1410 is estimated. The maximum-likelihoodcost model 1410 represents the relationship between the deployment costand the achievable performance, if no optimization of the deploymentoccurred. This relationship is non-linear, hence is modelled with anon-linear regression model, such as polynomial regression.

From the optimized [cost, performance] pairs 1408, the Pareto frontiermay be defined 1412 along the line of these optimal values. The systemand algorithms described thus far produces data representing theun-optimized and optimized forecasting performance 1414 for allinstrumented and un-instrumented substations at various levels ofinstrumented substation penetration, hence deployment cost.

FIG. 15 is a plot 1500 of the deployment cost 1502 versus the averageforecast accuracy 1504, showing the relationship between the optimizedgrid configuration 1506 and the unoptimized grid configurations 1508 ata range of cost levels. The corresponding Pareto frontier 1510 andmaximum likelihood unoptimized model 1512 are also shown.

From this data, the system can produce the plot 1500. This graph is thetool by which a user deploying a smart grid solution may plan for aninvestment level and determine the deployment configuration for thatinvestment level that results in optimal performance in the data-basedsimulation from previous deployments. The user picks a level for thedeployment cost 1502 that they are willing to accept. The model is usedto estimate associated optimal performance, or average forecastingaccuracy 1504, for that cost level. If the user is not satisfied withthe level of performance the cost is increased and the model is rerununtil an appropriate performance level is reached

Once the user is satisfied with the average forecasting accuracy 1504and the corresponding deployment cost 1502, they may retrieve theassociated optimized configuration or state feature. The user may thenuse the deployment strategy that resulted in that optimized performanceas a template for the deployment. The maximum likelihood modelrepresents the average-case performance they can expect if they do notuse the optimized deployment strategy. Hence, by using the optimizeddeployment strategy the user may increase the chances that theperformance they can expect will be higher than the maximum likelihoodperformance for that cost level.

FIG. 16 is a plot 1600 that may help a user to decide on a deploymentbudget 1602. For a given chosen budget level, they are also returned theoptimized grid configuration that resulted in that optimized performanceon the Pareto frontier 1604.

It may be noted that the simulation may be rerun manually orautomatically, for example, at regular intervals, to determine if theplacement of the instrumentation is optimized, or if it should bechanged. This may be useful, for example, if systems expand to moreusers or more substations. Further, if conditions change, such asincreased temperature in a region, the deployment simulation may bererun to determine if more instrumentation is needed.

FIG. 17 is a block diagram of a non-transitory, machine readable medium1700 including instructions, which when executed, direct a processor1702 to generate forecasts for noninstrumented substations and topredict forecasting accuracy for particular deployments. The processor1702 may access the non-transitory, machine readable medium 1700 over abus 1704. The processor 1702 and bus 1704 may be as described withrespect to FIG. 7. The non-transitory, machine readable medium 1700 mayinclude devices described for the mass storage 708 of FIG. 7 or mayinclude optical disks, thumb drives, or any number of other hardwaredevices.

The non-transitory, machine readable medium 1700 may include code 1706to direct the processor 1702 to generate forecasts for noninstrumentedstations, for example, as described with respect to FIGS. 8-11. Code1708 may be included to direct the processor 1702 to evaluate theprediction, for example, as described with respect to FIG. 8. Code 1710may be included to direct the processor 1702 to determine if criteriahave been met. Code 1712 may be included to direct the processor 1702 tocluster substations, for example, as described with respect to FIG. 11.Code 1714 may be included to direct the processor 1702 to store andretrieve historical data, for example, as described with respect to FIG.6. Code 1716 may be included to direct the processor 1702 to store andretrieve metadata, for example, as described with respect to FIG. 6.Code 1718 may be included to direct the processor 1702 to runperformance simulations to determine an optimum deployment ofinstrumentation to substations, for example, as described with respectto FIGS. 13-16.

Example 1 includes an apparatus, including a device to forecastperformance for a noninstrumented substation. The device has a storagedevice holds a historic database storing historic data for instrumentedsubstations. The storage device also holds a metadata database ofmetadata for the instrumented substations and noninstrumentedsubstations. An unsupervised machine learning manager generates acluster of substations by metadata, wherein at least a portion of thesubstations in the cluster are noninstrumented substations. A supervisedmachine learning manager generates a forecast for a noninstrumentedsubstation in the cluster from historic data generated from theinstrumented substations.

Example 2 includes the subject matter of example 1. In this example, thedevice includes a network interface controller (NIC).

Example 3 includes the subject matter of either of examples 1 or 2. Inthis example, the device includes a radio transceiver.

Example 4 includes the subject matter of any of examples 1 to 3. In thisexample, the device includes an auxiliary data source manager toretrieve data from an external database in a cloud, aninternet-of-things (IoT) device, or a sensor, or any combinationsthereof.

Example 5 includes the subject matter of example 4. In this example, theexternal database includes weather data.

Example 6 includes the subject matter of example 4. In this example, theexternal database includes metadata for substations.

Example 7 includes the subject matter of example 4. In this example, theexternal database includes scheduling data.

Example 8 includes the subject matter of example 4. In this example, theIoT device includes a traffic monitoring device.

Example 9 includes the subject matter of example 4. In this example, theIoT device includes a weather station.

Example 10 includes the subject matter of example 4. In this example,the IoT device includes a temperature sensor.

Example 11 includes the subject matter of any of examples 1 to 10. Inthis example, the apparatus includes a data aggregator to collect datafrom a number of instrumented substations.

Example 12 includes the subject matter of any of examples 1 to 11. Inthis example, the device includes a performance simulator to simulateforecasts of deployment strategies for instrumenting substations.

Example 13 includes the subject matter of example 12. In this example,the performance simulator creates a graph of deployment cost versusforecasting performance.

Example 14 includes the subject matter of any of examples 1 to 13. Inthis example, the device includes a sensor interface to couple to anexternal sensor.

Example 15 includes a method for forecasting data for a noninstrumentedsubstation. The method includes determining a cluster id for anoninstrumented substation, creating a model from historic data forinstrumented substations having the cluster id, and forecasting the datafor the noninstrumented substation from the model.

Example 16 includes the subject matter of example 15. In this example,determining the cluster id includes accessing metadata for a number ofsubstations, grouping the substations into a number of clusters, based,at least in part, on the metadata, and assigning the cluster id to eachof the clusters.

Example 17 includes the subject matter of either of examples 15 or 16.In this example, the substations may be grouped into clusters byselecting metadata features related to power consumption, and performinga clustering algorithm to create the clusters.

Example 18 includes the subject matter of any of examples 15 to 17. Inthis example, metadata features may include average power, averageinter-day correlation, area load classifications, work schedules,regional energy tariffs, weather data, seasonal data, or trafficpatterns, or any combinations thereof.

Example 19 includes the subject matter of any of examples 15 to 18. Inthis example, the method may include creating a vector of features,wherein the vector of features includes a binary vector of clusteringfeatures and a binary vector of forecasting features, simulating apredictive performance across all substations, and determining iftermination criteria have been met.

Example 20 includes the subject matter of any of examples 15 to 19. Inthis example, the method may include determining if the predictiveperformance has improved. If so, a current binary vector of clusteringfeatures may be copied into a best feature store. A random element inthe current binary vector of clustering features may be selected and afeature bit for the random element inverted, and the simulation may bererun.

Example 21 includes the subject matter of any of examples 15 to 20. Inthis example, creating the model includes aggregating the data for allof the instrumented substations having the cluster id, and creating themodel from the aggregated data.

Example 22 includes the subject matter of any of examples 15 to 21. Inthis example, creating the model includes creating a model for eachinstrumented substation having the cluster id, and performing anindividual forecast for a noninstrumented substation using each model.

Example 23 includes the subject matter of any of examples 15 to 22. Inthis example, forecasting the data may include aggregating theindividual forecast for each noninstrumented substation to form anaggregated forecast.

Example 24 includes the subject matter of any of examples 15 to 23. Inthis example, the method includes generating a forecast for aninstrumented substation.

Example 25 includes the subject matter of any of examples 15 to 24. Inthis example, the method may include creating a model for theinstrumented substation from the historic data, and generating theforecast.

Example 26 includes the subject matter of any of examples 15 to 25. Inthis example, the method includes determining a deployment strategy fordeploying instrumentation to a portion of a number of substations.

Example 27 includes the subject matter of any of examples 15 to 26. Inthis example, the method may include generating initial state features,iterating state features, evaluating average substation performance,determining if termination criteria have been met, and generating animplementation plan.

Example 28 includes the subject matter of any of examples 15 to 27. Inthis example, the method may include determining if cost constraintshave reached 100%, and, if not, iterating cost constraints by apredetermined amount.

Example 29 includes the subject matter of any of examples 15 to 28. Inthis example, the implementation plan may include a graph of averagesubstation performance versus deployment cost.

Example 30 includes a non-transitory, machine readable medium, includinginstructions to direct a processor to obtain metadata for a number ofsubstations including both instrumented and noninstrumented substations.Instructions are included to direct the processor to create a cluster ofsubstations based on the metadata, obtain historic data for theinstrumented substations, and generate a forecast for a noninstrumentedsubstation.

Example 31 includes the subject matter of example 30. In this example,the non-transitory, machine readable medium includes instructions todirect the processor to simulate performance for different distributionsof instrumentation across the substations. Instructions are included todirect the processor to determine if termination criteria are met, andcreate a graph of performance versus deployment cost for the differentdistributions of instrumentation.

Example 32 includes an apparatus, including a device to forecastperformance for a noninstrumented substation. The device includes astorage device to hold a historic database including historic data forinstrumented substations. The storage device to hold a metadata databaseincluding metadata for the instrumented substations and fornoninstrumented substations. The device includes a means for forecastingvalues for a noninstrumented substation from historic data generatedfrom the instrumented substations.

Example 33 includes the subject matter of example 32. In this example,the apparatus includes a means to retrieve data from an externaldatabase.

Example 34 includes the subject matter of either of examples 32 or 33.In this example, the apparatus includes a means to obtain data fromexternal sensors.

Example 35 includes the subject matter of any of examples 32 to 34. Inthis example, the apparatus includes a means to aggregate data from anumber of instrumented substations.

Example 36 includes the subject matter of any of examples 32 to 35. Inthis example, the device includes a means to simulate forecasts ofdeployment strategies for instrumenting substations.

Example 37 includes a non-transitory, machine-readable medium includinginstructions to direct a processor in a node to perform any one of themethods of examples 15 to 29.

Example 38 includes an apparatus including means to perform any one ofthe methods of examples 15 to 29.

Example 39 includes the subject matter of any one of examples 1 to 14.In this example, the historic data comprises active power (P), reactivepower (Q), voltage, apparent power (S), or voltage-ampere reactive(VAR), or any combinations thereof.

Example 40 includes the subject matter of any one of examples 15 to 29.In this example, the method includes measuring historic data for theinstrumented substations, wherein the historic data comprises activepower (P), reactive power (Q), voltage, apparent power (S), orvoltage-ampere reactive (VAR), or any combinations thereof.

Example 41 includes the subject matter of any one of examples 15 to 29.In this example, the method includes forecasting an equipment failure ina noninstrumented substation, based, at least in part, on valuesmeasured at the instrumented substations for reactive power (Q),voltage, apparent power (S), or voltage-ampere reactive (VAR), or anycombinations thereof.

Some embodiments may be implemented in one or a combination of hardware,firmware, and software. Some embodiments may also be implemented asinstructions stored on a machine-readable medium, which may be read andexecuted by a computing platform to perform the operations describedherein. A machine-readable medium may include any mechanism for storingor transmitting information in a form readable by a machine, e.g., acomputer. For example, a machine-readable medium may include read onlymemory (ROM); random access memory (RAM); magnetic disk storage media;optical storage media; flash memory devices; or electrical, optical,acoustical or other form of propagated signals, e.g., carrier waves,infrared signals, digital signals, or the interfaces that transmitand/or receive signals, among others.

An embodiment is an implementation or example. Reference in thespecification to “an embodiment,” “one embodiment,” “some embodiments,”“various embodiments,” or “other embodiments” means that a particularfeature, structure, or characteristic described in connection with theembodiments is included in at least some embodiments, but notnecessarily all embodiments, of the techniques. The various appearancesof “an embodiment”, “one embodiment”, or “some embodiments” are notnecessarily all referring to the same embodiments. Elements or aspectsfrom an embodiment can be combined with elements or aspects of anotherembodiment.

Not all components, features, structures, characteristics, etc.described and illustrated herein need be included in a particularembodiment or embodiments. If the specification states a component,feature, structure, or characteristic “may”, “might”, “can” or “could”be included, for example, that particular component, feature, structure,or characteristic is not required to be included. If the specificationor claim refers to “a” or “an” element, that does not mean there is onlyone of the element. If the specification or claims refer to “anadditional” element, that does not preclude there being more than one ofthe additional element.

It is to be noted that, although some embodiments have been described inreference to particular implementations, other implementations arepossible according to some embodiments. Additionally, the arrangementand/or order of circuit elements or other features illustrated in thedrawings and/or described herein need not be arranged in the particularway illustrated and described. Many other arrangements are possibleaccording to some embodiments.

In each system shown in a figure, the elements in some cases may eachhave a same reference number or a different reference number to suggestthat the elements represented could be different and/or similar.However, an element may be flexible enough to have differentimplementations and work with some or all of the systems shown ordescribed herein. The various elements shown in the figures may be thesame or different. Which one is referred to as a first element and whichis called a second element is arbitrary.

The techniques are not restricted to the particular details listedherein. Indeed, those skilled in the art having the benefit of thisdisclosure will appreciate that many other variations from the foregoingdescription and drawings may be made within the scope of the presenttechniques. Accordingly, it is the following claims including anyamendments thereto that define the scope of the techniques.

What is claimed is:
 1. An apparatus, comprising a device to forecastperformance for a noninstrumented substation, comprising: a storagedevice comprising a historic database comprising historic data forinstrumented substations; the storage device comprising a metadatadatabase comprising metadata for the instrumented substations and fornoninstrumented substations; an unsupervised machine learning manager togenerate a cluster of substations by metadata, wherein at least aportion of the substations in the cluster are noninstrumentedsubstations; and a supervised machine learning manager to generate aforecast for a noninstrumented substation in the cluster from historicdata generated from the instrumented substations, wherein generating theforecast comprises: creating a vector of features, wherein the vector offeatures comprises a binary vector of clustering features and a binaryvector of forecasting features: simulating a predictive performanceacross all substations; and determining if termination criteria havebeen met to terminate the simulation.
 2. The apparatus of claim 1,wherein the historic data comprises active power (P), reactive power(Q), voltage, apparent power (S), or voltage-ampere reactive (VAR), orany combinations thereof.
 3. The apparatus of claim 1, wherein thedevice comprises an auxiliary data source manager to retrieve data froman external database in a cloud, an internet-of-things (IoT) device, ora sensor, or any combinations thereof.
 4. The apparatus of claim 3,wherein the external database comprises weather data, metadata forsubstations, or scheduling data, or any combinations thereof.
 5. Theapparatus of claim 3, wherein the IoT device comprises a trafficmonitoring device, a weather station, or a temperature sensor, or anycombinations thereof.
 6. The apparatus of claim 1, wherein the devicecomprises a performance simulator to simulate forecasts of deploymentstrategies for instrumenting substations.
 7. The apparatus of claim 6,wherein the performance simulator creates a graph of deployment costversus forecasting performance.
 8. A method for forecasting data for anoninstrumented substation, comprising: accessing metadata for aplurality of substations; grouping the plurality of substations into aplurality of dusters, based, at least in part, on the metadata; andassigning the cluster id to each of the plurality of dusters;determining a cluster id for the noninstrumented substation; creating amodel from historic data for instrumented substations having the dusterid; and forecasting the data for the noninstrumentcd substation from themodel; creating a vector of features, wherein the vector of featurescomprises a binary vector of clustering features and a binary vector offorecasting features; simulating a predictive performance across allsubstations; and determining if termination criteria have been met. 9.The method of claim 8, comprising measuring historic data for theinstrumented substations, wherein the historic data comprises activepower (P), reactive power (Q), voltage, apparent power (S), orvoltage-ampere reactive (VAR), or any combinations thereof.
 10. Themethod of claim 8, comprising forecasting an equipment failure in anoninstrumentcd substation, based, at least in part, on values measuredat the instrumented substations for reactive power (Q), voltage,apparent power (S), or voltage-ampere reactive (VAR), or anycombinations thereof.
 11. The method of claim 8, wherein grouping theplurality of substations into the plurality of clusters comprises:selecting metadata features related to power consumption; and performinga clustering algorithm to create the clusters.
 12. The method of claim11, wherein the metadata features comprise average power, averageinter-day correlation, area load classifications, work schedules,regional energy tariffs, weather data, seasonal data, or trafficpatterns, or any combinations thereof.
 13. The method of claim 8,comprising: determine if the predictive performance has improved; and,if so, copy a current binary vector of clustering features into a bestfeature store; select a random element in the current binary vector ofclustering features and Inver a feature bit; and rerun the simulation.14. The method of claim 8, wherein creating the model comprises:aggregating the data for all of the instrumented substations having thecluster id; and creating the model from the aggregated data.
 15. Themethod of claim 8, wherein creating the model comprises: creating amodel for each instrumented substation having the cluster id; andperforming an individual forecast for the noninstrumented substationusing each model.
 16. The method of claim 15, Wherein forecasting thedata comprises aggregating the individual forecast for eachnoninstrumented substation to form an aggregated forecast.
 17. Themethod of claim 8, comprising generating a forecast for an instrumentedsubstation.
 18. The method of claim 17, comprising: creating a model forthe instrumented substation from the historic data; and generating theforecast.
 19. The method of claim 8, comprising determining a deploymentstrategy for deploying instrumentation to a portion of the plurality ofsubstations.
 20. The method of claim 19 comprising: generating initialstate features; iterating state features; evaluating average substationperformance; determining if termination criteria have been met; andgenerating implementation plan.
 21. The method of claim 20, wherein theimplementation plan comprises a graph of average substation performanceversus deployment cost.
 22. A non-transitory, machine readable medium,comprising instructions, which when executed, direct a processor to:obtain metadata for a plurality of substations comprising bothinstrumented and noninstrumented substations; create a cluster ofsubstations based on the metadata; obtain historic data for theinstrumented substations; generate a forecast for a noninstrumentedsubstation; create a vector of features, wherein the vector of featurescomprises a binary vector of clustering features and a binary vector offorecasting features; simulate a predictive performance across theplurality of substations; and determining if termination criteria havebeen met.
 23. The non-transitory, machine readable medium of claim 22,comprising instructions, which when executed, direct the processor to:simulate performance for different distributions of instrumentationacross the plurality of substations; determine if termination criteriaare met; and create a graph of performance versus deployment cost forthe different, distributions of instrumentation.